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Precisely pinpointing the positions of emitters within the diffraction limit is crucial for quantitative analysis or molecular mechanism investigation in biomedical research but has remained challenging unless exploiting single molecule localization microscopy (SMLM). Via integrating experimental spot dataset with deep learning, we develop a new approach, Digital-SMLM, to accurately predict emitter numbers and positions for sub-diffraction-limit spots with an accuracy of up to 98 % and a root mean square error as low as 14 nm. Digital-SMLM can accurately resolve two emitters at a close distance, e.g. 30 nm. Digital-SMLM outperforms Deep-STORM in predicting emitter numbers and positions for sub-diffraction-limited spots and recovering the ground truth distribution of molecules of interest. We have validated the generalization capability of Digital-SMLM using independent experimental data. Furthermore, Digital-SMLM complements SMLM by providing more accurate event number and precise emitter positions, enabling SMLM to closely approximate the natural state of high-density cellular structures.

• An essential marker for subsequent illness study and diagnosis is the histopathological picture. • Classes U-Net combines the information entropy classification with U-Net and U-Net++ architecture for the segmentation of photoacoustic histology images. • Our Classes U-Net can effectively improve the segmentation accuracy and reduce the required computing resources. • It is expected to further improve the accuracy of model segmentation, reduce the amount of model calculation, and study the impact of differences between photoacoustic data and other data. Due to its ability to reveal the intrinsic molecule specificity of DNA/RNA at the subcellular lateral resolution, photoacoustic (PA) microscopy holds great promise in histopathology imaging of tissue samples. An essential marker for subsequent illness study and diagnosis is the histopathological picture. Segmenting the histopathological image of cell nuclei has been significantly aided by contemporary image processing technology, while they usually suffer from inadequate segmentation or training resource waste. There are many traditional methods, such as threshold segmentation and region-growing segmentation, but these traditional segmentation methods are affected by the gray distribution of the image itself, so the accuracy of segmentation is difficult to meet the requirements of the index. To address this challenge, we propose an approach, called Classes U-Net, which combines the information entropy classification with U-Net and U-Net++ architecture for the segmentation of photoacoustic histology images. The results show that our Classes U-Net effectively improves the DICE to 91.43 %, IOU to 84.215, better than U-Net's 83.28 % and 71.35 %, better than U-Net++'s 84.60 % and 73.31 %, and our Classes U-Net reduce the required computing resources. [ABSTRACT FROM AUTHOR]

Real-time traffic speed prediction is an essential component of intelligent transportation systems applications on large-scale urban networks, e.g., proactive traffic management, advanced information provision, and prompt incident response. The family of traffic prediction models (e.g., convolutional neural networks) based on multi-detector speed diagrams in the time-space plane has been one of the most frequently used approaches for individual roads and the entire network. However, the predefined stacking sequence of traffic detectors along the spatial dimension of the speed diagram has a significant influence on the prediction performance, which makes network-wide speed prediction more challenging. To tackle the above challenge and better capture complicated traffic dynamics, we propose a novel speed prediction approach, named spatial–temporal deep tensor neural networks (ST-DTNN), for a large-scale urban network with mixed road types. Spatial and temporal dependencies of different road segments are simultaneously taken into account to improve the network-wide prediction accuracy. A scalable deep tensor is constructed for the ST-DTNN to eliminate the potentially negative impact caused by the manually stacking sequence of speed time series collected at different locations. Multi-step ahead traffic speeds can be simultaneously predicted based on probe data for a real-world large-scale urban network with hundreds of detectors installed on freeways, highways, and major/minor arterials. The results demonstrate the capability and effectiveness of the proposed ST-DTNN approach. Compared with the benchmark models, the ST-DTNN performs higher prediction accuracy during either peak or off-peak periods within an acceptable training time and has more stable prediction performance on the spatial scale. The proposed approach can be extended to develop network-wide traffic state monitoring, optimize routing in navigation services, and support congestion mitigation. [ABSTRACT FROM AUTHOR]